I’ve been a bit mad preparing for an upcoming conference, so I haven’t had a lot of time lately to blog about interesting developments in the conservation world. However, it struck me today that my preparations provide ideal material for a post about the future of Africa’s biodiversity.

While decidedly fortunate to be invited, I am a bit intimidated by the line-up of big brains that will be attending, and of the fact that I know next to bugger all about African mammals (in a conservation science sense, of course). Still, apparently my insight as an outsider and ‘global’ thinker might be useful, so I’ve been hard at it the last few weeks planning my talk and doing some rather interesting analyses. I want to share some of these with you now beforehand, although I won’t likely give away the big prize until after I return to Australia.

I’ve been asked to talk about human population pressures on (southern) African mammal species, which might seem simple enough until you start to delve into the complexities of just how human populations affect wildlife. It’s simply from the perspective that human changes to the environment (e.g., deforestation, agricultural expansion, hunting, climate change, etc.) do cause species to dwindle and become extinct faster than they otherwise would (hence the entire field of conservation science). However, it’s another thing entirely to attempt to predict what might happen decades or centuries down the track. Read the rest of this entry »

There’s no question at all that science communication has never before been so widespread and of such high quality. More and more scientists and science students are now blogging, tweeting and generally engaging the world about their science findings. There is also an increasing number of professional science communication associations out there, and a growing population of professional science communicators. It is possibly the best time in history to be involved in the generation and/or communication of scientific results.

You couldn’t really do ecology if you didn’t know how to construct even the most basic mathematical model — even a simple regression is a model (the non-random relationship of some variable to another). The good thing about even these simple models is that it is fairly straightforward to interpret the ‘strength’ of the relationship, in other words, how much variation in one thing can be explained by variation in another. Provided the relationship is real (not random), and provided there is at least some indirect causation implied (i.e., it is not just a spurious coincidence), then there are many simple statistics that quantify this strength — in the case of our simple regression, the coefficient of determination (R2) statistic is a usually a good approximation of this.

When you go beyond this correlative model approach and start constructing more mechanistic models that emulate ecological phenomena from the bottom-up, things get a little more complicated when it comes to quantifying the strength of relationships. Perhaps the most well-known category of such mechanistic models is the humble population viability analysis, abbreviated to PVA§.

Let’s take the simple case of a four-parameter population model we could use to project population size over the next 10 years for an endangered species that we’re introducing to a new habitat. We’ll assume that we have the following information: the size of the founding (introduced) population (n), the juvenile survival rate (Sj, proportion juveniles surviving from birth to the first year), the adult survival rate (Sa, the annual rate of surviving adults to year 1 to maximum longevity), and the fertility rate of mature females (m, number of offspring born per female per reproductive cycle). Each one of these parameters has an associated uncertainty (ε) that combines both measurement error and environmental variation.

If we just took the mean value of each of these three demographic rates (survivals and fertility) and project a founding population of n = 10 individuals for 1o years into the future, we would have a single, deterministic estimate of the average outcome of introducing 10 individuals. As we already know, however, the variability, or stochasticity, is more important than the average outcome, because uncertainty in the parameter values (ε) will mean that a non-negligible number of model iterations will result in the extinction of the introduced population. This is something that most conservationists will obviously want to minimise.

So each time we run an iteration of the model, and generally for each breeding interval (most often 1 year at a time), we choose (based on some random-sampling regime) a different value for each parameter. This will give us a distribution of outcomes after the 10-year projection. Let’s say we did 1000 iterations like this; taking the number of times that the population went extinct over these iterations would provide us with an estimate of the population’s extinction probability over that interval. Of course, we would probably also vary the size of the founding population (say, between 10 and 100), to see at what point the extinction probability became acceptably low for managers (i.e., as close to zero as possible), but not unacceptably high that it would be too laborious or expensive to introduce that many individuals. Read the rest of this entry »

Dick’s latest paper in Molecular Ecology is a meta-analysis designed to test whether there are any genetic grounds for NOT attempting genetic rescue for inbreeding-depressed populations. I suppose a few definitions are in order here. Genetic rescue is the process, either natural or facilitated, where inbred populations (i.e., in a conservation sense, those comprising too many individuals bonking their close relatives because the population in question is small) receive genes from another population such that their overall genetic diversity increases. In the context of conservation genetics, ‘inbreeding depression‘ simply means reduced biological fitness (fertility, survival, longevity, etc.) resulting from parents being too closely related.

Seems like an important thing to avoid, so why not attempt to facilitate gene flow among populations such that those with inbreeding depression can be ‘rescued’? In applied conservation, there are many reasons given for not attempting genetic rescue: Read the rest of this entry »

Global human society is a massive, consumptive beast that on average degrades its life-support system. As we’ve recently reported, this will only continue to get worse in the decades to centuries to come. Some have argued that as long as we can develop our societies enough, the impact of this massive demographic force can be lessened – a concept described by the environmental Kuznets curve. However, there is little evidence that negative societal impact on the environment is lessened as per capita wealth exceeds some threshold; unfortunately environmental damage tends to, on average, increase as a nation’s net wealth increases. That’s not to say that short-term improvements cannot be achieved through technological innovation – in fact, they will be essential to offset the inexorable growth of the global human population.

In this vein, I just stumbled across an extremely interesting paper today published online early in Conservation Biology that describes trends in charismatic wildlife (i.e., big mammals) as the former Soviet Union collapsed in 1991 and societal breakdown ensued. The authors had access to an amazing dataset that spanned the decade prior to the collapse, the decade immediately following, and a subsequent decade of societal renewal. What they found was fascinating. Read the rest of this entry »

Like many academics, I’m more or less convinced that I am somewhere on the mild end of the autism spectrum. No, I haven’t been diagnosed and I doubt very much that my slight ‘autistic’ tendencies have altered my social capacity, despite my wife claiming that I have only two emotions – angry or happy. Nor have they engendered any sort of idiot savant mathematical capability.

But I’m reasonably comfortable with mathematics, I can do a single task for hours once it consumes my attention, and I’m excited about discovering how things work. And I love to code. Rather than academics having a higher innate likelihood of being ‘autistic’, I just think the job attracts such personalities.

In the past few years though, my psychological state is probably less dictated by the hard-wiring of my ‘autidemic’ mind and more and more influenced by the constant battery of negative information my brain receives.